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All Journal TEPIAN Buletin Poltanesa
Mahmudi, Ama Muzni
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education

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User-Centric Waste Management Through Reward-Based Digital Systems Mahmudi, Ama Muzni; Mantoro , Teddy
Poltanesa Vol 26 No 1 (2025): June 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i1.3380

Abstract

The effective management of household and urban waste is a critical challenge in achieving sustainability within the fields of waste management and circular economy practices. Traditional waste sorting systems face inefficiencies due to low user participation, limited accountability, and inadequate transparency in tracking and reporting waste contributions. This research addresses these challenges by introducing a reward-based digital platform that incentivizes users to sort and deposit waste at designated collection points. The platform assigns points based on the type and quantity of waste submitted, tracks contributions, and provides detailed reports to users, fostering transparency and trust. The proposed solution demonstrates potential to increase user engagement, improve waste sorting accuracy, and enhance reporting capabilities, supporting the transition toward sustainable and circular waste management systems.
AI for Enhanced Efficiency in Business Waste Sorting Strategies Mahmudi, Ama Muzni; Mantoro , Teddy
TEPIAN Vol. 6 No. 3 (2025): September 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i3.3379

Abstract

As the global waste crisis grows, businesses are under pressure to improve waste management. AI, especially through machine learning and image recognition, offers innovative solutions for optimizing waste sorting. By using Convolutional Neural Networks (CNNs) and deep learning models trained on extensive datasets of waste images, companies can automate the classification of materials such as plastic, glass, and metal with high accuracy. This reduces reliance on manual labor, minimizes human error, and improves the speed and precision of sorting. Cameras capture images of waste items on conveyor belts, which are then analyzed by AI algorithms in real time. These systems continuously improve through feedback loops and reinforcement learning, leading to more efficient sorting over time. The result is higher recycling rates, reduced operational costs, and enhanced sustainability outcomes. AI-based systems enable businesses to decrease waste sent to landfills, recover valuable materials, and lower costs associated with waste management. With continuous updates to their training data and the use of edge computing for real-time processing, these solutions represent a major advancement in sustainable business practices.
Co-Authors Mantoro , Teddy